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evaluate_1.py
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evaluate_1.py
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"""
Evaluation for mvtec_ad dataset.
Reference from https://github.com/denguir/student-teacher-anomaly-detection.
Author: Luyao Chen
Date: 2022.09
"""
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
import cv2
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
from PIL import Image
from new_student import _Teacher, TeacherOrStudent,StudentTrans
from mvtec_dataset import MVTec_AD
from fast_dense_feature_extractor import *
def error(student_outputs, teacher_output):
# n*imH*imW*d
# s_mean = 0
# for s_out in student_outputs:
# s_mean += s_out
# s_mean /= len(student_outputs)
s_mean = torch.mean(student_outputs, dim=1)
return torch.norm(s_mean - teacher_output, dim=3)
def variance(student_outputs):
# s_sum = 0
# for s_out in student_outputs:
# s_sum += s_out
# s_mean = s_sum / len(student_outputs)
# v = 0
# for s_out in student_outputs:
# v += torch.norm(s_out - s_mean, dim=3)
# v /= len(student_outputs)
sse = torch.sum(student_outputs ** 2, dim=4)
msse = torch.mean(sse, dim=1)
s_mean = torch.mean(student_outputs, dim=1)
var = msse - torch.sum(s_mean**2, dim=3)
return var
def increment_mean_and_var(mu_N, var_N, N, batch):
'''Increment value of mean and variance based on
current mean, var and new batch
'''
# batch: (batch, h, w, vector)
B = batch.size()[0] # batch size
# we want a descriptor vector -> mean over batch and pixels
mu_B = torch.mean(batch, dim=[0, 1, 2])
S_B = B * torch.var(batch, dim=[0, 1, 2], unbiased=False)
S_N = N * var_N
mu_NB = N / (N + B) * mu_N + B / (N + B) * mu_B
S_NB = S_N + S_B + B * mu_B**2 + N * mu_N**2 - (N + B) * mu_NB**2
var_NB = S_NB / (N + B)
return mu_NB, var_NB, N + B
if __name__ == "__main__":
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
# add more size for multi-scale segmentation
patch_sizes = [17] #17,33,65
# num of studetns per teacher
num_students = 1 #1
# image height and width should be multiples of sL1∗sL2∗sL3...
imH = 128
imW = 128
batch_size = 1
print(os.getcwd())
small_batch = 2048
work_dir = 'work_dir/'
class_dir = 'cable/'
class_dir1 = 'cable/'
# /home/DISCOVER_summer2022/liwz/data/MVTec/bottle
# 0：bottle etc.
labels = torch.ones((1))*0
train_dataset_dir = '~/data/MVTec/' + class_dir1 + 'train/'
test_dataset_dir = '~/data/MVTec/' + class_dir1
device = torch.device('cuda')
N_scale = len(patch_sizes)
std = [0.229, 0.224, 0.225]
mean = [0.485, 0.456, 0.406]
trans = transforms.Compose([
# transforms.RandomCrop((imH, imW)),
transforms.Resize((imH, imW)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
mask_trans = transforms.Compose([
# transforms.RandomCrop((imH, imW)),
transforms.Resize((imH, imW), Image.NEAREST),
transforms.ToTensor(),
])
anomaly_free_dataset = datasets.ImageFolder(
train_dataset_dir, transform=trans)
af_dataloader = DataLoader(anomaly_free_dataset, batch_size=batch_size)
test_dataset = MVTec_AD(test_dataset_dir, transform=trans,
mask_transform=mask_trans, phase='test')
test_dataloader = DataLoader(test_dataset, batch_size=batch_size)
# FDFE池化
multiprocess = multiPoolPrepare(17, 17)
teachers = []
students = []
for patch_size in patch_sizes:
_teacher = _Teacher(patch_size)
checkpoint = torch.load(work_dir + '_teacher' +
str(patch_size) + '.pth', torch.device('cpu'))
_teacher.load_state_dict(checkpoint)
teacher = TeacherOrStudent(patch_size, _teacher, imH, imW).to(device)
teacher.eval()
teachers.append(teacher)
s_t = []
for i in range(num_students):
# issue #2. must create a new _teacher.
_teacher = _Teacher(patch_size)
#
student = StudentTrans()
student = nn.DataParallel(student).to(device)
#
checkpoint = torch.load(work_dir + '/studenttrans' +
str(patch_size) + '_' + str(i) +
'.pth', torch.device('cpu'))
student.load_state_dict(checkpoint)
student.eval()
s_t.append(student)
students.append(s_t)
with torch.no_grad():
t_mu, t_var, t_N = [0 for i in range(N_scale)], [0 for i in range(N_scale)], [
0 for i in range(N_scale)]
print('Callibrating teacher on train dataset.')
for data, _ in tqdm(af_dataloader):
data = data.to(device)
for i in range(N_scale):
t_out = teachers[i](data)
t_mu[i], t_var[i], t_N[i] = increment_mean_and_var(t_mu[i], t_var[i], t_N[i], t_out)
# mu_err, var_err = torch.tensor([4.7920308113098145]), torch.tensor([3.410670280456543])
# mu_var, var_var = torch.tensor([4.074430465698242]), torch.tensor([1.5367100238800049])
max_err, max_var = [0 for i in range(N_scale)], [0 for i in range(N_scale)]
mu_err, var_err, N_err = [0 for i in range(N_scale)], [0 for i in range(N_scale)], [0 for i in range(N_scale)]
mu_var, var_var, N_var = [0 for i in range(N_scale)], [0 for i in range(N_scale)], [0 for i in range(N_scale)]
print('Callibrating scoring parameters on train dataset.')
for data, _ in tqdm(af_dataloader):
data = data.to(device)
#
labels = labels.to(device).long()
for i in range(N_scale):
teacher_output = (teachers[i](data) - t_mu[i])/torch.sqrt(t_var[i])
student_outputs = []
for j in range(num_students):
#
data1 = multiprocess(data)
new_data = nn.Unfold(17, 1)(data1)
bz = data.size(0)
x = new_data.transpose(2, 1).contiguous()
x = x.view(bz*imH * imW, 3, 17, 17)
output=torch.zeros(bz*imH * imW,128).to(device)
for i1 in range(bz*imH*imW//small_batch):
ndata = x[i1*small_batch:(i1*small_batch+small_batch)]
nlabels = labels.repeat(small_batch)
a = students[i][j](ndata, nlabels)
output[i1 * small_batch:(i1 * small_batch + small_batch), :]=a
student_outputs.append(output.view(bz,imH,imW,128))
# loss不变
student_outputs = torch.stack(student_outputs, dim=1)
e = error(student_outputs, teacher_output)
v = variance(student_outputs)
mu_err[i], var_err[i], N_err[i] = increment_mean_and_var(
mu_err[i], var_err[i], N_err[i], e)
mu_var[i], var_var[i], N_var[i] = increment_mean_and_var(
mu_var[i], var_var[i], N_var[i], v)
max_err[i] = max(max_err[i], torch.max(e))
max_var[i] = max(max_var[i], torch.max(v))
max_score = 0
for i in range(N_scale):
print('mu_err:{}, var_err:{}, mu_var:{}, var_var:{}'.format(
mu_err[i], var_err[i], mu_var[i], var_var[i]
))
max_score += (max_err[i] - mu_err[i]) / torch.sqrt(var_err[i]+1e-6) + \
(max_var[i] - mu_var[i]) / torch.sqrt(var_var[i]+1e-6)
max_score /= N_scale
print('max_score:{}'.format(max_score))
score_map_list = []
gt_mask_list = []
img_id = 0
for data, gt_mask, _ in tqdm(test_dataloader):
plt_list = []
ori_imgs = data
data = data.to(device)
labels=labels.to(device).long()
gt_mask_list.append(gt_mask.data.numpy())
anomaly_score = 0
for i in range(N_scale):
teacher_output = (teachers[i](
data) - t_mu[i]) / torch.sqrt(t_var[i])
plt_list.append(teacher_output)
student_outputs = []
for j in range(num_students):
#
data1 = multiprocess(data)
new_data = nn.Unfold(17, 1)(data1)
bz = data.size(0)
x = new_data.transpose(2, 1).contiguous()
x = x.view(bz * imH * imW, 3, 17, 17)
output = torch.zeros(bz * imH * imW, 128).to(device)
for i1 in range(bz * imH * imW // small_batch):
ndata = x[i1 * small_batch:(i1 * small_batch + small_batch)]
nlabels = labels.repeat(small_batch)
output[i1 * small_batch:(i1 * small_batch + small_batch), :] = students[i][j](ndata, nlabels)
student_outputs.append(output.view(bz, imH, imW, 128))
plt_list.append(student_outputs[j])
student_outputs = torch.stack(student_outputs, dim=1)
e = error(student_outputs, teacher_output)
v = variance(student_outputs)
anomaly_score += (e - mu_err[i]) / torch.sqrt(var_err[i]+1e-6) + \
(v - mu_var[i]) / torch.sqrt(var_var[i]+1e-6)
anomaly_score /= N_scale
score_map_list.append(anomaly_score.cpu().detach().numpy())
# print('max:{:.2f},min:{:.2f},avg:{:.2f}'.format(torch.max(anomaly_score),
# torch.min(anomaly_score),
# torch.mean(anomaly_score)))
# plt.figure()
# plt.subplot(2, 2, 1)
# plt_img = plt_list[1].cpu().detach().numpy()[0]
# plt_img = np.mean(plt_img, axis=2)
# plt_img = np.expand_dims(plt_img, 2)
# plt.imshow(plt_img, cmap='jet')
# plt.colorbar()
# plt.subplot(2, 2, 2)
# plt_img = plt_list[2].cpu().detach().numpy()[0]
# plt_img = np.mean(plt_img, axis=2)
# plt_img = np.expand_dims(plt_img, 2)
# plt.imshow(plt_img, cmap='jet')
# plt.colorbar()
# plt.subplot(2, 2, 3)
# plt_img = plt_list[3].cpu().detach().numpy()[0]
# plt_img = np.mean(plt_img, axis=2)
# plt_img = np.expand_dims(plt_img, 2)
# plt.imshow(plt_img, cmap='jet')
# plt.colorbar()
# plt.subplot(2, 2, 4)
# plt_img = plt_list[0].cpu().detach().numpy()[0]
# plt_img = np.mean(plt_img, axis=2)
# plt_img = np.expand_dims(plt_img, 2)
# plt.imshow(plt_img, cmap='jet')
# plt.colorbar()
# plt.savefig('cmp.png')
# plt.close()
# px = 118
# py = 132
# plt.figure(figsize=(6, 3))
# plt_vec = plt_list[1].cpu().detach().numpy()[0, px, py]
# plt_vec -= plt_list[0].cpu().detach().numpy()[0, px, py]
# plt.plot(plt_vec, label='s1')
# plt_vec = plt_list[2].cpu().detach().numpy()[0, px, py]
# plt.plot(plt_vec, label='s2')
# plt_vec = plt_list[3].cpu().detach().numpy()[0, px, py]
# plt.plot(plt_vec, label='s3')
# plt_vec = plt_list[0].cpu().detach().numpy()[0, px, py]
# plt.plot(plt_vec, label='t')
# plt.legend()
# plt.savefig('vec.png')
# plt.close()
anomaly_score -= torch.min(anomaly_score)
# anomaly_score /= torch.max(anomaly_score)
anomaly_score /= max_score
# anomaly_score /= 30
score_map = anomaly_score.cpu().detach().numpy()[0, :, :]
score_map = np.minimum(score_map, 1)
score_map = cv2.applyColorMap(
np.uint8(score_map * 255), cv2.COLORMAP_JET)
# # cv2.imwrite('score.jpg', score_map)
ori_img = ori_imgs.permute(0, 2, 3, 1).detach().numpy()[0, :, :, :]
for c in range(3):
ori_img[:, :, c] = ori_img[:, :, c] * std[c] + mean[c]
ori_img = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
# # cv2.imwrite('ori.jpg', np.uint8(ori_img * 255))
save_img = np.concatenate(
(np.uint8(ori_img * 255), score_map), axis=1)
# cv2.imwrite('res.jpg', save_img)
cv2.imwrite('tmp/' + str(img_id) + '.jpg', save_img)
img_id += 1
flatten_gt_mask_list = np.concatenate(gt_mask_list).ravel()
flatten_score_map_list = np.concatenate(score_map_list).ravel()
per_pixel_rocauc = roc_auc_score(
flatten_gt_mask_list, flatten_score_map_list)
print('pixel ROCAUC:{}'.format(per_pixel_rocauc))